PDCNN: FRAMEWORK for Potato Diseases Classification Based on Feed Foreword Neural Network

• Main Authors: Israa Mohammed Hassoon, Samar Amil Qassir, Musaab Riyadh
• Format: Article
• Language: Arabic
• Published: College of Science for Women, University of Baghdad 2021-06-01
• Series: Baghdad Science Journal
• Subjects: K-means; Gray Level Run Length Matrix; First Order Histogram Features; Scaled Conjugate Gradient Backpropagation
• Online Access: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/3823

         The economy is exceptionally reliant on agricultural productivity. Therefore, in domain of agriculture, plant infection discovery is a vital job because it gives promising advance towards the development of agricultural production. In this work, a framework for potato diseases classification based on feed foreword neural network is proposed. The objective of this work  is presenting a system that can detect and classify four kinds of potato tubers diseases; black dot, common scab, potato virus Y and early blight based on their images. The presented PDCNN framework comprises three levels: the pre-processing is first level, which is based on K-means clustering algorithm to detect the infected area from potato image. The second level is features extraction which extracts features from the infected area based on hybrid features: grey level run length matrix and 1st order histogram based features. The attributes that extracted from second level are utilized in third level using FFNN to perform the classification process. The proposed framework is applied to database with different backgrounds, totally 120 color potato images, (80) samples used in training the network and the rest samples (40) used for testing. The proposed PDCNN framework is very effective in classifying four types of potato tubers diseases with 91.3% of efficiency.